How to represent Tic-Tac-Toe vs Checkers or Chess for a Neural Network
When you are working with neural networks, as long as the data is there, the network is usually able to learn how to process it into a useful result, you usually also want to keep the amount of weights to a minimum. When you you use extra weights, it will take longer to train the network because you need to tune even more values for an optimal network. So, for tic-tac-toe, any of your solutions involving 9 inputs should work just fine. Also, it helps if you keep the inputs between 0 and 1 if you are using log sigmoid, and -1 and 1 if you are using hyperbolic tangent for your activation function. You can probably easily figure out what to use for other activation functions. You can take your data and transform it into another dataset with values within a specific range through a process called range normalization. For chess, you can simply encode every piece in several different ways, and it will probably not make that much of a difference. The general rule of thumb is you want to minimize the amount of weights while still giving the most possible variables to the network.